{"id":"W4224862171","doi":"10.1021/acs.est.1c05543","title":"<i>In Vivo</i> Bioconcentration of 10 Anionic Surfactants in Rainbow Trout Explained by <i>In Vitro</i> Data on Partitioning and S9 Clearance","year":2022,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"ARC Resources (Canada); The Scarborough Hospital; University of Toronto; ASL Environmental Sciences (Canada)","funders":"European Chemical Industry Council","keywords":"Bioconcentration; Bioaccumulation; Biotransformation; Chemistry; Rainbow trout; Partition coefficient; In vivo; Environmental chemistry; Toxicokinetics; Reaction rate constant; Membrane; In vitro; Pulmonary surfactant; Persistent organic pollutant; Chromatography; Toxicity; Biochemistry; Kinetics; Fish <Actinopterygii>; Pollutant; Organic chemistry; Biology; Enzyme","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005226185,0.0004369079,0.0001958386,0.0003430052,0.0001987044,0.0005413108,0.0004285684,0.0004514264,0.002526493],"category_scores_gemma":[0.0006305213,0.0004080227,0.0006455004,0.000214305,0.0005652908,0.0003484255,0.0002235078,0.0006658403,0.00107417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121256,"about_ca_system_score_gemma":0.0009775138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01907142,"about_ca_topic_score_gemma":0.01677634,"domain_scores_codex":[0.9997577,0.00004070981,0.00002474613,0.00008518706,0.00004678642,0.00004475134],"domain_scores_gemma":[0.9996426,0.00008571049,0.00009738752,0.00006399347,0.00007638241,0.0000339465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001400319,0.00006692109,0.008078723,0.0001008922,0.00007152514,0.00007812884,0.00004567832,0.001582556,0.9831624,0.0006228668,0.0005450152,0.004244928],"study_design_scores_gemma":[0.00005701584,0.0009773358,0.03201964,0.00002056716,0.0001312442,0.0002660405,0.00009431302,0.008267742,0.9554662,0.0005086232,0.002162264,0.00002901614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762247,0.002260838,0.01410499,0.0004189408,0.0001167267,0.00006705834,0.00139636,0.0002473298,0.005163028],"genre_scores_gemma":[0.9892096,0.0007774114,0.002960846,0.0001373016,0.00001147951,0.00004128285,0.001310173,0.00005657148,0.005495354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01907142,"threshold_uncertainty_score":0.03792077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138209571807249,"score_gpt":0.256804595221484,"score_spread":0.2429836380407591,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}